COVID-19 Clinical Footprint to Infer About Mortality

Autor:	Carlos E. Rodríguez, Ramsés H. Mena
Rok vydání:	2022
Předmět:	Methodology (stat.ME) FOS: Computer and information sciences Statistics and Probability Economics and Econometrics Applications (stat.AP) Statistics Probability and Uncertainty Statistics - Applications Statistics - Methodology Social Sciences (miscellaneous)
Zdroj:	Journal of the Royal Statistical Society Series A: Statistics in Society. 185:S547-S572
ISSN:	1467-985X 0964-1998
DOI:	10.1111/rssa.12947
Popis:	Information of 1.6 million patients identified as SARS-CoV-2 positive in Mexico is used to understand the relationship between comorbidities, symptoms, hospitalizations and deaths due to the COVID-19 disease. Using the presence or absence of these latter variables a clinical footprint for each patient is created. The risk, expected mortality and the prediction of death outcomes, among other relevant quantities, are obtained and analyzed by means of a multivariate Bernoulli distribution. The proposal considers all possible footprint combinations resulting in a robust model suitable for Bayesian inference. 23 pages and 6 figures
Databáze:	OpenAIRE
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